Summary of Short-term Wind Speed Forecasting For Power Integration in Smart Grids Based on Hybrid Lssvm-svmd Method, by Ephrem Admasu Yekun et al.
Short-term Wind Speed Forecasting for Power Integration in Smart Grids based on Hybrid LSSVM-SVMD Method
by Ephrem Admasu Yekun, Alem H. Fitwib, Selvi Karpaga Subramaniand, Anubhav Kumard, Teshome Goa Tella
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A hybrid machine learning approach is proposed for predicting short-term wind speed, which is crucial for integrating wind power into the grid system. The approach combines Successive Variational Mode Decomposition (SVMD), Least Squares Support Vector Machines (LSSVM) with optimized hyperparameters using a variant of Quantum-behaved Particle Swarm Optimization (QPSO), and long short-term model (LSTM). The method is tested on two separate datasets from a local wind farm, showing significant improvements in performance compared to state-of-the-art benchmark models. Specifically, the proposed method achieves a 1.21% to 32.76% reduction in root mean square error (RMSE) and a 2.05% to 40.75% reduction in mean average error (MAE). The entire code implementation is freely available on Github. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict wind speed is being developed for the power grid. This method uses a combination of techniques like decomposing data into smaller parts and using special kinds of machines learning models. It’s tested on real-world data from a local wind farm and performs better than other methods. The results show that this approach can reduce errors by 1-40%. You can find all the code for this project on Github. |
Keywords
» Artificial intelligence » Lstm » Machine learning » Mae » Optimization
